Wind Turbine Performance in Controlled Conditions: BEM Modeling and Comparison with Experimental Results
Bibliographic record
Abstract
Predictions of the performance of operating wind turbines are challenging for many reasons including the unsteadiness of the wind and uncertainties in blade aerodynamic behaviour. In the current study an extended blade element momentum (BEM) program was developed to compute the rotor power of an existing 4.3 m diameter turbine and compare predictions with reported controlled experimental measurements. Beginning with basic blade geometry and the iterative computation of aerodynamic properties, the method integrated the BEM analysis into the program workflow ensuring that the power production by a blade element agreed with its lift and drag data at the same Reynolds number. The parametric study using the extended BEM algorithm revealed the close association of the power curve behaviour with the aerodynamic characteristics of the blade elements, the discretization of the aerodynamic span, and the dependence on Reynolds number when the blades were stalled. Transition prediction also affected overall performance, albeit to a lesser degree. Finally, to capture blade finite area effects, the tip loss model was adjusted depending on stall conditions. The experimental power curve for the HAWT of the current study was closely matched by the extended BEM simulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".